3,454 research outputs found

    Automated syntactic mediation for Web service integration

    No full text
    As the Web Services and Grid community adopt Semantic Web technology, we observe a shift towards higher-level workflow composition and service discovery practices. While this provides excellent functionality to non-expert users, more sophisticated middleware is required to hide the details of service invocation and service integration. An investigation of a common Bioinformatics use case reveals that the execution of high-level workflow designs requires additional processing to harmonise syntactically incompatible service interfaces. In this paper, we present an architecture to support the automatic reconciliation of data formats in such Web Service worklflows. The mediation of data is driven by ontologies that encapsulate the information contained in heterogeneous data structures supplying a common, conceptual data representation. Data conversion is carried out by a Configurable Mediator component, consuming mappings between \xml schemas and \owl ontologies. We describe our system and give examples of our mapping language against the background of a Bioinformatics use case

    From source to target and back: symmetric bi-directional adaptive GAN

    Get PDF
    The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

    Lynx X-Ray Observatory: An Overview

    Get PDF
    Lynx, one of the four strategic mission concepts under study for the 2020 Astrophysics Decadal Survey, provides leaps in capability over previous and planned x-ray missions and provides synergistic observations in the 2030s to a multitude of space- and ground-based observatories across all wavelengths. Lynx provides orders of magnitude improvement in sensitivity, on-axis subarcsecond imaging with arcsecond angular resolution over a large field of view, and high-resolution spectroscopy for point-like and extended sources in the 0.2- to 10-keV range. The Lynx architecture enables a broad range of unique and compelling science to be carried out mainly through a General Observer Program. This program is envisioned to include detecting the very first seed black holes, revealing the high-energy drivers of galaxy formation and evolution, and characterizing the mechanisms that govern stellar evolution and stellar ecosystems. The Lynx optics and science instruments are carefully designed to optimize the science capability and, when combined, form an exciting architecture that utilizes relatively mature technologies for a cost that is compatible with the projected NASA Astrophysics budget

    Android Malware Family Classification Based on Resource Consumption over Time

    Full text link
    The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe. With respect to DroidScribe, our approach is easier to reproduce. Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices. The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior. Our approach relies on resource consumption metrics available from the proc file system. Features are extracted through detrended fluctuation analysis and correlation. Finally, a SVM is employed to classify malware into families. We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe. Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.Comment: Extended Versio

    Automatic generation of user interfaces from rigorous domain and use case models

    Get PDF
    Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

    PRIS: Practical robust invertible network for image steganography

    Full text link
    Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/
    corecore